Electronic and microprocessor feedback control for automotive systems are seeing increasing attention. The interest in feedback control systems stems from the availability of powerful but inexpensive microprocessors that enable real time performance and efficiency monitoring and control. The most common example may be found in automotive fuel injection systems, which have found a widely accepted use in many IC engine applications.
The goal of a control system is to affect the output of physical systems in desired manners and/or to achieve certain tasks, using available control inputs to physical systems. A control system can be broken into two categories: open loop systems and closed loop (or feedback) systems. FIG. 1 depicts a generic diagram of an open loop control system. The control input 50 is used to impart forces or effects on the physical system 51, whose dynamics are described by a mathematical input-output relationship or model H, so that desired dynamic behavior of the output 52 can be achieved. The design method of control input function is simply computing the inverse of the model H (i.e., H.sup.-1) and multiplying it by the time history of the desired output. This type of open loop control system is commonly used for many real engineering systems, including automotive carburetors, various manufacturing processes, and space shuttle trajectory calculation. The problems associated with this approach are the fact that no mathematical model of a physical system is one hundred percent accurate and the fact that almost all physical systems are affected by unknown disturbances. FIG. 2 depicts a generic diagram illustrating these problems. Since the actual model relationship H cannot be determined, only the best estimate of the model H is available for the purpose of designing the control input 60. Since the actual model H comprises the best model estimate H and the modeling error dH, the control input 60 affects both blocks 61 and 63. Then, the actual output 64 will comprise of both the control input multiplied by the model 61 and modeling error 63, and thus, the output will be quite different than the desired output. Furthermore, the disturbance d also affect the whole system and particularity the output 64. In fact the modeling error 63 and the disturbance 62 can be quite large and the output can deviate quite appreciably from their desired level, and in extreme cases, the modeling error 63 and the disturbance 62 can destabilize the actual physical system. Therefore, the open loop control scheme discussed heretofore is not a robust method of controlling a physical system.
The application of feedback or closed loop control can remedy this problem. FIG. 3 depicts a generic schematic diagram of a feedback control system. Since the output signal 72 contains information of both disturbance and modeling errors, it can be used to affect the input signal to the physical system so that a desired output can be obtained. This is done dynamically and automatically by the control electronics and/or software contained in a microcomputer 71. Then, the control input is automatically adjusted according to varying output and disturbance conditions via the feedback path 70, so that a desired output can be achieved at all times even in the presence of modeling errors and disturbances.
The objective of automotive fuel control systems is to control the air to fuel ratio entering the combustion chamber, so that proper combustion can take place. Since the amount of fuel entering the intake manifold and subsequently the combustion chamber is controlled by the throttle actuated by the driver (or by the cruise control actuator), the object is to determine the rate of air entering the combustion chamber, determine the corresponding intake valve open duration as a function of engine speed, and spray the proper amount of fuel.
Two common methods exist for determining the rate of air entering combustion chambers. The first of the two methods is the speed-density method, and a typical embodiment of this method is depicted in FIG. 4. The best estimate of the mass rate of air m.sub.ao entering the combustion chamber is determined by knowing the density of air in the intake manifold 81, the total volume of the air in the intake manifold, the engine speed 82, and calibrated data of physical transfer characteristics between the intake manifold piping and valves and the combustion chamber 83 (known as the volumetric efficiency). The total mass of air in the intake manifold is determined from signal outputs from the manifold absolute temperature (MAT) sensor 84 and the manifold absolute pressure (MAP) sensor 85 as is known in the art. The volumetric efficiency function is a measure of the efficiency of an engine's induction system and is defined as the actual volume entering the combustion chamber 83 divided by the actual physical volume displaced by the piston 88. The volumetric efficiency is a complex function of both intake and exhaust manifold ducting as well as the engine speed and the total pressure in the intake manifold. In general, analytic form of this function cannot be determined, and as a result, extensive calibration must be performed both statically on dynamometer and dynamically on vehicle. How and under what conditions to calibrate are determined heuristically, based on the experience of the design engineer, and a large number (a few hundred to a few thousand) of datum points are generated. The table of datum points are stored in the engine control module (ECM) 86 that contains one or more microcomputers. The desired fuel injection rate is calculated by the ECM 86, and the calculated fuel injection rate is commanded to the fuel injector 87.
The inaccuracies and noise associated with the engine speed sensor 82, MAT sensor 84, and MAP sensor 85, as well as unavoidable errors in calibrating the volumetric efficiency function will result in poor control of air to fuel ratio. The oxygen sensor 89 located at the exhaust manifold 90 is used to compensate the modeling and sensor inaccuracies. The oxygen sensor detects the presence of oxygen in the exhaust gas and outputs a high voltage when the intake air to fuel mass ratio is lower (or richer) than the stoichiometric ratio of 14.64:1, and a low voltage when the intake air to fuel mass ratio is higher (or leaner) than the stoichiometric ratio. The oxygen sensor characteristics are largely binary, rather than continuous, as depicted in FIG. 5. In addition, the sensor output voltages vary significantly with the exhaust gas temperature. Therefore, the oxygen sensor 89 is not an ideal sensor for feedback control. However, the oxygen sensor 89 is the most common feedback sensor, because the cost of continuous range sensors is prohibitive for automotive applications. Furthermore, the oxygen sensor output lags the actual air to fuel ratio information to be controlled by two engine revolutions, time for air to travel down the exhaust pipe to reach the oxygen sensor, and the physical time lag associated with the sensor itself. As a result of the imperfect feedback sensor, existing feedback control theories cannot be used to determine the structure or the gains of the feedback controller that needs to be implemented in the ECM 86. Therefore, heuristic means of determining the controller structure and calibrating the feedback gains are used, and in general a large number (a few hundred to a few thousand) of feedback gains must be calibrated through a trial and error process for every different automobile system. Therefore, IC engine fuel injection control systems are a collection of numerous control schemes designed for very specific operating conditions and a heuristically determined method of scheduling when to switch to different schemes. How and under what conditions to calibrate as well as when to switch to different control schemes is determined heuristically, based on the experience of the design engineer.
The second of the two popular methods is the mass air flow meter (MAFM) method, and a typical embodiment of this method is depicted in FIG. 6. The best estimate of the mass rate of air m.sub.ao entering the combustion chamber is determined by measuring the mass rate of air m.sub.ai entering the intake manifold. The mass rate of air m.sub.ai entering the intake manifold is measured by the use of the mass air flow meter 102 located near the entrance of the intake manifold. While this MAFM method is more straightforward than the previously described speed-density method, the MAFM method is generally more expensive due to the relatively high cost of the MAFM sensor 102. The best estimate of the mass rate of air m.sub.ao entering the combustion chamber is the mass rate of air m.sub.ai entering the intake manifold delayed by the time constant of the MAFM sensor itself and the time constant associated with filling and emptying the intake manifold. The time constant associated with filling and emptying the intake manifold may be ignored, or for better control accuracy, may be estimated as a function of the intake manifold temperature and pressure conditions, the engine speed, and the volumetric efficiency function. If this second option is used the engine speed sensor 82, the MAT sensor 84, and the MAP sensor 85 of the speed-density method embodiment discussed in FIG. 4 must also be used. As in the speed-density method, the desired fuel injection rate in the MAFM method is calculated by the ECM 105, and the calculated fuel injection rate is commanded to the fuel injector 106.
The inaccuracies and noise associated with the MAFM sensor 102, as well as in calculating the mass rate of air m.sub.ao entering the combustion chamber from the mass rate of air m.sub.ai entering the intake manifold, will result in inaccurate control of air to fuel ratio. The oxygen sensor 107 at the exhaust manifold 108 is used to compensate the modeling and sensor inaccuracies as in the speed-density method previously described. The problems and requirements associated with the use of oxygen sensor 107 for feedback control and the need for using heuristic means for determining the controller structure, calibrating the feedback gains, and scheduling the gains are the same as previously described in the speed-density method.
In both the speed density method or the mass air flow meter method, determining the structure and gains of the fuel injection controller is extremely difficult and time consuming. Gain and event scheduling is an art known to a very few. Furthermore, because of the heuristic approach combined with trial and error process employed in developing fuel injection controllers, the performance levels are always sub-optimal, and the algorithms are not robust to conditions that are not considered in the development process.
A nonlinear fuel injection controller design methodology that is globally effective (rather than locally controlled and gain and event scheduled scheduled as described heretofore) is also known and is detailed in "A Nonlinear Controller Design Method for Fuel-Injected Automotive Engines", Cho and Hedrick, Transactions of the ASME, Journal of Engineering for Gas Turbines and Power, Volume 110, July 1988, and in "Sliding Mode Fuel Injection Controller: Its Advantages", Cho and Hedrick, presented at the ASME Winter Annual Meeting, Chicago, December 1988, and to be published in the Transactions of the ASME, Journal of Dynamic Systems, Measurement and Control, September 1991. However, the nonlinear controller or sliding mode controller methodology described by Cho and Hedrick (both references) has a significant problem that makes it impractical for implementation. Specifically, the methodology requires the knowledge of the actual amount of fuel entering combustion chambers at a given time; this is necessary because of the fuel delivery dynamics, the actual amount of fuel entering combustion chambers is not identical to the amount of fuel being sprayed by the fuel injector. This information can be obtained in two ways. The first is to install a sensor that can measure the actual amount of fuel entering combustion chambers; this type of sensors are typically very expensive, and thus, automotive applications of this type of sensors is impractical. The second is to analytically estimate the actual amount of fuel entering combustion chambers; this can be accomplished by observer or filter techniques. However, a closed loop observer or filter cannot be constructed because the inability to use feedback sensors for the same reasons as the first alternative. The use of open loop observers or filters is prohibitive because of the robustness problems associated with such configurations; this problem is analogous to the robustness problems of open loop controllers described previously in FIGS. 1 and 2.
Accordingly, it is an object of this invention to provide methodologies for implementing the nonlinear global control system for automotive fuel injection systems, so to overcome the difficulties, poor performance, and costly development associated with the conventional, event and gain scheduled linear local control systems used in current IC engines and to overcome the difficulties and problems associated with the nonlinear or sliding mode automotive fuel injection controller design method disclosed by Cho and Hedrick.
Is a further object of this invention to provide methods of designing designing automotive fuel injection controllers that are robust, i.e., maintain the system stability, even under severe operating conditions and in the presence of modeling and estimation errors and sensor noise.
It is still another object of this invention to provide methods of designing automotive fuel injection controllers that are globally effective, with the feedback gains automatically adjusting to varying driver commands and changing environmental conditions, thus eliminating the need to extensively gain and event schedule numerous local controllers. Hence, the new method is much easier and less time consuming to calibrate, tune, and develop than any previously known methods.
It is still another object of this invention to provide methods of designing automotive fuel injection controllers that are optimal for a given set of sensors, system parameters, and system models.